.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/snsx/plot_kdsplot_script.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_snsx_plot_kdsplot_script.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_snsx_plot_kdsplot_script.py:


plot_kdsplot_script with examples
==========================================

An example showing the :py:func:`~scikitplot.snsx.kdsplot` function
used by a scikit-learn regressor.

.. GENERATED FROM PYTHON SOURCE LINES 8-12

.. code-block:: Python
   :lineno-start: 9


    # Authors: The scikit-plots developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 13-14

Import scikit-plot

.. GENERATED FROM PYTHON SOURCE LINES 14-17

.. code-block:: Python
   :lineno-start: 14

    import scikitplot.snsx as sp









.. GENERATED FROM PYTHON SOURCE LINES 18-29

.. code-block:: Python
   :lineno-start: 18

    import numpy as np; np.random.seed(0)  # reproducibility
    import pandas as pd

    # Create a DataFrame with predictions
    df = pd.DataFrame({
        "y_true": np.random.normal(0.5, 0.1, 100).round(),
        "y_score": np.random.normal(0.5, 0.15, 100),
        "hue": np.random.normal(0.5, 0.4, 100).round(),
    })









.. GENERATED FROM PYTHON SOURCE LINES 30-34

.. code-block:: Python
   :lineno-start: 30

    p = sp.kdsplot(df, x="y_true", y="y_score", kind="df", n_deciles=10, round_digits=2)
    p







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>decile</th>
          <th>prob_min</th>
          <th>prob_max</th>
          <th>prob_avg</th>
          <th>cnt_resp</th>
          <th>cnt_resp_total</th>
          <th>cnt_resp_non</th>
          <th>cnt_resp_wiz</th>
          <th>cnt_resp_rndm</th>
          <th>rate_resp</th>
          <th>cum_resp</th>
          <th>cum_resp_pct</th>
          <th>cum_resp_total</th>
          <th>cum_resp_total_pct</th>
          <th>cum_resp_non</th>
          <th>cum_resp_non_pct</th>
          <th>cum_resp_wiz</th>
          <th>cum_resp_wiz_pct</th>
          <th>KS</th>
          <th>lift</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>0.76</td>
          <td>0.86</td>
          <td>0.79</td>
          <td>5.0</td>
          <td>10.0</td>
          <td>5.0</td>
          <td>NaN</td>
          <td>5.5</td>
          <td>50.0</td>
          <td>5.0</td>
          <td>9.09</td>
          <td>10.0</td>
          <td>10.0</td>
          <td>5.0</td>
          <td>11.11</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>-2.02</td>
          <td>0.91</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>0.64</td>
          <td>0.73</td>
          <td>0.68</td>
          <td>7.0</td>
          <td>10.0</td>
          <td>3.0</td>
          <td>10.0</td>
          <td>5.5</td>
          <td>70.0</td>
          <td>12.0</td>
          <td>21.82</td>
          <td>20.0</td>
          <td>20.0</td>
          <td>8.0</td>
          <td>17.78</td>
          <td>10.0</td>
          <td>18.18</td>
          <td>4.04</td>
          <td>1.09</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>0.60</td>
          <td>0.64</td>
          <td>0.63</td>
          <td>6.0</td>
          <td>10.0</td>
          <td>4.0</td>
          <td>10.0</td>
          <td>5.5</td>
          <td>60.0</td>
          <td>18.0</td>
          <td>32.73</td>
          <td>30.0</td>
          <td>30.0</td>
          <td>12.0</td>
          <td>26.67</td>
          <td>20.0</td>
          <td>36.36</td>
          <td>6.06</td>
          <td>1.09</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>0.56</td>
          <td>0.60</td>
          <td>0.59</td>
          <td>9.0</td>
          <td>10.0</td>
          <td>1.0</td>
          <td>10.0</td>
          <td>5.5</td>
          <td>90.0</td>
          <td>27.0</td>
          <td>49.09</td>
          <td>40.0</td>
          <td>40.0</td>
          <td>13.0</td>
          <td>28.89</td>
          <td>30.0</td>
          <td>54.55</td>
          <td>20.20</td>
          <td>1.23</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>0.50</td>
          <td>0.56</td>
          <td>0.53</td>
          <td>4.0</td>
          <td>10.0</td>
          <td>6.0</td>
          <td>10.0</td>
          <td>5.5</td>
          <td>40.0</td>
          <td>31.0</td>
          <td>56.36</td>
          <td>50.0</td>
          <td>50.0</td>
          <td>19.0</td>
          <td>42.22</td>
          <td>40.0</td>
          <td>72.73</td>
          <td>14.14</td>
          <td>1.13</td>
        </tr>
        <tr>
          <th>5</th>
          <td>6</td>
          <td>0.45</td>
          <td>0.50</td>
          <td>0.48</td>
          <td>5.0</td>
          <td>10.0</td>
          <td>5.0</td>
          <td>10.0</td>
          <td>5.5</td>
          <td>50.0</td>
          <td>36.0</td>
          <td>65.45</td>
          <td>60.0</td>
          <td>60.0</td>
          <td>24.0</td>
          <td>53.33</td>
          <td>50.0</td>
          <td>90.91</td>
          <td>12.12</td>
          <td>1.09</td>
        </tr>
        <tr>
          <th>6</th>
          <td>7</td>
          <td>0.40</td>
          <td>0.44</td>
          <td>0.42</td>
          <td>4.0</td>
          <td>10.0</td>
          <td>6.0</td>
          <td>5.0</td>
          <td>5.5</td>
          <td>40.0</td>
          <td>40.0</td>
          <td>72.73</td>
          <td>70.0</td>
          <td>70.0</td>
          <td>30.0</td>
          <td>66.67</td>
          <td>55.0</td>
          <td>100.00</td>
          <td>6.06</td>
          <td>1.04</td>
        </tr>
        <tr>
          <th>7</th>
          <td>8</td>
          <td>0.37</td>
          <td>0.40</td>
          <td>0.39</td>
          <td>3.0</td>
          <td>10.0</td>
          <td>7.0</td>
          <td>0.0</td>
          <td>5.5</td>
          <td>30.0</td>
          <td>43.0</td>
          <td>78.18</td>
          <td>80.0</td>
          <td>80.0</td>
          <td>37.0</td>
          <td>82.22</td>
          <td>55.0</td>
          <td>100.00</td>
          <td>-4.04</td>
          <td>0.98</td>
        </tr>
        <tr>
          <th>8</th>
          <td>9</td>
          <td>0.32</td>
          <td>0.36</td>
          <td>0.34</td>
          <td>6.0</td>
          <td>10.0</td>
          <td>4.0</td>
          <td>0.0</td>
          <td>5.5</td>
          <td>60.0</td>
          <td>49.0</td>
          <td>89.09</td>
          <td>90.0</td>
          <td>90.0</td>
          <td>41.0</td>
          <td>91.11</td>
          <td>55.0</td>
          <td>100.00</td>
          <td>-2.02</td>
          <td>0.99</td>
        </tr>
        <tr>
          <th>9</th>
          <td>10</td>
          <td>0.17</td>
          <td>0.32</td>
          <td>0.28</td>
          <td>6.0</td>
          <td>10.0</td>
          <td>4.0</td>
          <td>0.0</td>
          <td>5.5</td>
          <td>60.0</td>
          <td>55.0</td>
          <td>100.00</td>
          <td>100.0</td>
          <td>100.0</td>
          <td>45.0</td>
          <td>100.00</td>
          <td>55.0</td>
          <td>100.00</td>
          <td>0.00</td>
          <td>1.00</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python
   :lineno-start: 35

    p = sp.kdsplot(df, x="y_true", y="y_score",kind="lift")




.. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_001.png
   :alt: Lift Curve
   :srcset: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 38-40

.. code-block:: Python
   :lineno-start: 38

    p = sp.kdsplot(df, x="y_true", y="y_score",kind="lift_decile_wise")




.. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_002.png
   :alt: Lift Decile-wise Curve
   :srcset: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 41-43

.. code-block:: Python
   :lineno-start: 41

    p = sp.kdsplot(df, x="y_true", y="y_score",kind="cumulative_gain")




.. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_003.png
   :alt: Cumulative Gain Curve
   :srcset: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 44-46

.. code-block:: Python
   :lineno-start: 44

    p = sp.kdsplot(df, x="y_true", y="y_score",kind="ks_statistic")




.. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_004.png
   :alt: KS Statistic
   :srcset: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 47-48

.. code-block:: Python
   :lineno-start: 47

    p = sp.kdsplot(df, x="y_true", y="y_score",kind="report", verbose=True)



.. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_005.png
   :alt: Lift Curve, Lift Decile-wise Curve, Cumulative Gain Curve, KS Statistic
   :srcset: /auto_examples/snsx/images/sphx_glr_plot_kdsplot_script_005.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    {
      "decile": "Ranked group (1=highest probability). Why: shows model discrimination. Fatal if top deciles don't capture positives.",
      "prob_min": "Lowest predicted probability in the decile. Why: signals calibration. Fatal if too close to prob_max (model not ranking well).",
      "prob_max": "Highest predicted probability in the decile. Why: checks spread. Fatal if overlaps lower deciles (poor separation).",
      "prob_avg": "Average predicted probability in the decile. Why: good for calibration curves. Fatal if averages do not increase with decile.",
      "cnt_resp": "Number of true responders. Why: measures captured positives. Fatal if counts are flat across deciles (model useless).",
      "cnt_resp_total": "Total samples in the decile. Why: denominator for rates. Fatal if deciles differ in size — signals incorrect binning.",
      "cnt_resp_non": "Number of non-responders. Why: tracks negatives. Fatal if too high in top deciles (bad ranking).",
      "cnt_resp_wiz": "Ideal responders if model was perfect (sorted by actuals). Why: sets maximum benchmark. Fatal if actual is far below this.",
      "cnt_resp_rndm": "Expected responders if random. Why: baseline. Fatal if model only slightly above random.",
      "rate_resp": "Response rate (cnt_resp / total). Why: decile quality. Fatal if early deciles do not outperform later ones.",
      "cum_resp": "Cumulative responders up to decile. Why: shows capture rate. Fatal if curve is too shallow.",
      "cum_resp_pct": "Cumulative responder percentage. Why: needed for lift/gain charts. Fatal if curve near random line.",
      "cum_resp_total": "Cumulative samples. Why: population coverage. Fatal if distribution biased.",
      "cum_resp_total_pct": "Cumulative population percentage. Why: axis for gain/ROC curves. Fatal if deciles unbalanced.",
      "cum_resp_non": "Cumulative non-responders. Why: tracks negatives. Fatal if they dominate early deciles.",
      "cum_resp_non_pct": "Cumulative non-responder %. Why: used in KS. Fatal if almost same as cum_resp_pct (model fails).",
      "cum_resp_wiz": "Cumulative ideal responders. Why: theoretical max. Fatal gap means weak targeting.",
      "cum_resp_wiz_pct": "Cumulative % ideal responders. Why: benchmark. Fatal if actual far below.",
      "KS": "Kolmogorov-Smirnov statistic. Why: measures max separation. Fatal if very low (<0.2). Strong models often 0.3-0.5.",
      "lift": "Cumulative lift vs random. Why: shows model gain. Fatal if <2 in top decile (weak business value)."
    }
       decile  prob_min  prob_max  ...  cum_resp_wiz_pct         KS      lift
    0       1  0.757001  0.857472  ...               NaN  -2.020202  0.909091
    1       2  0.642088  0.731452  ...         18.181818   4.040404  1.090909
    2       3  0.602239  0.641672  ...         36.363636   6.060606  1.090909
    3       4  0.561119  0.601465  ...         54.545455  20.202020  1.227273
    4       5  0.504775  0.559401  ...         72.727273  14.141414  1.127273
    5       6  0.446901  0.502622  ...         90.909091  12.121212  1.090909
    6       7  0.400478  0.437957  ...        100.000000   6.060606  1.038961
    7       8  0.370816  0.398850  ...        100.000000  -4.040404  0.977273
    8       9  0.324031  0.363077  ...        100.000000  -2.020202  0.989899
    9      10  0.166490  0.316185  ...        100.000000   0.000000  1.000000

    [10 rows x 20 columns]





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.777 seconds)


.. _sphx_glr_download_auto_examples_snsx_plot_kdsplot_script.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
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        :alt: Launch binder
        :width: 150 px

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        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_kdsplot_script.ipynb <plot_kdsplot_script.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_kdsplot_script.py <plot_kdsplot_script.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_kdsplot_script.zip <plot_kdsplot_script.zip>`


.. include:: plot_kdsplot_script.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_